Zhenheng Wang
Papers
1
Total Citations
12
H-Index
1
About
Zhenheng Wang is a researcher focused on advancing underwater robot perception through innovative deep learning and optimization techniques. His primary research areas include underwater target detection, lightweight neural network design, and knowledge distillation. Wang’s major contribution lies in developing a dynamic sampling transformer-based algorithm that significantly improves the accuracy and stability of object detection in challenging underwater environments, where poor image quality and complex lighting conditions often hinder traditional methods. By integrating knowledge-distillation optimization, his work enables high-performance detection models to be deployed on resource-constrained underwater platforms. His most cited paper, "Lightweight Underwater Target Detection Algorithm Based on Dynamic Sampling Transformer and Knowledge-Distillation Optimization" (2023), has garnered 12 citations, reflecting growing interest in efficient underwater perception systems. Wang’s research addresses a critical gap in practical underwater robotics, offering solutions that balance computational efficiency with detection robustness. His work is particularly notable for tackling real-world deployment challenges, making autonomous underwater vehicles more reliable for tasks such as marine exploration, environmental monitoring, and underwater infrastructure inspection.
Research Focus
Key Achievements
Top Papers
- 1